
'''simple docstring'''
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


@skip_mps
class 			SCREAMING_SNAKE_CASE__      (     snake_case_							,       unittest.TestCase):
				lowerCAmelCase_     			=							VideoToVideoSDPipeline
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"""video"""}) - {"""image""", """width""", """height"""}
				lowerCAmelCase_     			=							TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""video"""}) - {"""image"""}
				lowerCAmelCase_     			=							PipelineTesterMixin.required_optional_params - {"""latents"""}
				lowerCAmelCase_     			=							False

				# No `output_type`.
				lowerCAmelCase_     			=							frozenset(
				    [
				        """num_inference_steps""",
				        """generator""",
				        """latents""",
				        """return_dict""",
				        """callback""",
				        """callback_steps""",
				    ])







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											torch.manual_seed(0     )
											UpperCamelCase   			=     UNetaDConditionModel(
											    block_out_channels=(32, 64, 64, 64)						,				layers_per_block=2						,				sample_size=32						,				in_channels=4						,				out_channels=4						,				down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')						,				up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')						,				cross_attention_dim=32						,				attention_head_dim=4						,				)
											UpperCamelCase   			=     DDIMScheduler(
											    beta_start=0.00_085						,				beta_end=0.012						,				beta_schedule='scaled_linear'						,				clip_sample=A_						,				set_alpha_to_one=A_						,				)
											torch.manual_seed(0     )
											UpperCamelCase   			=     AutoencoderKL(
											    block_out_channels=[32, 64]						,				in_channels=3						,				out_channels=3						,				down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']						,				up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']						,				latent_channels=4						,				sample_size=128						,				)
											torch.manual_seed(0     )
											UpperCamelCase   			=     CLIPTextConfig(
											    bos_token_id=0						,				eos_token_id=2						,				hidden_size=32						,				intermediate_size=37						,				layer_norm_eps=1e-05						,				num_attention_heads=4						,				num_hidden_layers=5						,				pad_token_id=1						,				vocab_size=1000						,				hidden_act='gelu'						,				projection_dim=512						,				)
											UpperCamelCase   			=     CLIPTextModel(A_     )
											UpperCamelCase   			=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

											UpperCamelCase   			=     {
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=0     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     floats_tensor((1, 3, 3, 32, 32)						,				rng=random.Random(A_     )     ).to(A_     )

											if str(A_     ).startswith('mps'     ):
																		UpperCamelCase   			=     torch.manual_seed(A_     )
											else:
																		UpperCamelCase   			=     torch.Generator(device=A_     ).manual_seed(A_     )
											UpperCamelCase   			=     {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'video': video,
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'pt',
											}
											return inputs







				def 						UpperCAmelCase_    (       self     )->		str:







											'''simple docstring'''






											UpperCamelCase   			=     'cpu'  # ensure determinism for the device-dependent torch.Generator
											UpperCamelCase   			=     self.get_dummy_components()
											UpperCamelCase   			=     VideoToVideoSDPipeline(**A_     )
											UpperCamelCase   			=     sd_pipe.to(A_     )
											sd_pipe.set_progress_bar_config(disable=A_     )

											UpperCamelCase   			=     self.get_dummy_inputs(A_     )
											UpperCamelCase   			=     'np'
											UpperCamelCase   			=     sd_pipe(**A_     ).frames
											UpperCamelCase   			=     frames[0][-3:, -3:, -1]

											assert frames[0].shape == (32, 32, 3)
											UpperCamelCase   			=     np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]     )

											assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1e-2







				@unittest.skipIf(
				    torch_device != 'cuda' or not is_xformers_available()						,				reason='XFormers attention is only available with CUDA and `xformers` installed'						,				)
				def 						UpperCAmelCase_    (       self     )->		Tuple:







											'''simple docstring'''






											self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=A_						,				expected_max_diff=5e-3     )







				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
				def 						UpperCAmelCase_    (       self     )->		Optional[Any]:







											'''simple docstring'''






											pass







				@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'     )
				def 						UpperCAmelCase_    (       self     )->		Union[str, Any]:







											'''simple docstring'''






											pass







				@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'     )
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											pass







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											return super().test_progress_bar()


@slow
@skip_mps
class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'						,				torch_dtype=torch.floataa     )
											pipe.enable_model_cpu_offload()

											# 10 frames
											UpperCamelCase   			=     torch.Generator(device='cpu'     ).manual_seed(0     )
											UpperCamelCase   			=     torch.randn((1, 10, 3, 1024, 576)						,				generator=A_     )
											UpperCamelCase   			=     video.to('cuda'     )

											UpperCamelCase   			=     'Spiderman is surfing'

											UpperCamelCase   			=     pipe(A_						,				video=A_						,				generator=A_						,				num_inference_steps=3						,				output_type='pt'     ).frames

											UpperCamelCase   			=     np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656]     )
											assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array     ).sum() < 1e-2












"""simple docstring"""
from ..utils import DummyObject, requires_backends



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	List[Any]				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	Any ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Optional[int]				,    *UpperCamelCase_ :	Tuple				,    **UpperCamelCase_ :	List[Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	List[Any]				,    *UpperCamelCase_ :	str				,    **UpperCamelCase_ :	str ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Optional[int]				,    *UpperCamelCase_ :	Optional[int]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Tuple				,    *UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	Optional[Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	Optional[int]				,    *UpperCamelCase_ :	List[Any]				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	List[str]				,    **UpperCamelCase_ :	Tuple ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	Tuple				,    *UpperCamelCase_ :	Any				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )



class        UpperCamelCase__ (				metaclass=a_):







    """simple docstring"""
    __UpperCAmelCase           = ["""flax""", """transformers"""]







    def __init__(		self :	Optional[Any]				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Dict ):





           '''simple docstring'''

           requires_backends(self				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Dict				,    **UpperCamelCase_ :	List[str] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )







    @classmethod
    def 				a__			(		cls :	str				,    *UpperCamelCase_ :	Optional[Any]				,    **UpperCamelCase_ :	Union[str, Any] ):





           '''simple docstring'''

           requires_backends(cls				,    ['flax', 'transformers'] )